Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
To troubleshoot an AI model on an Ascend processor, first prove that the exact product, SoC, CANN release, runtime, framework, model format, and shape mode are a supported combination. Then reproduce conversion with the correct soc_version, establish numerically correct inference, and use operator- and system-level profiles to locate slow or inaccurate work. Compatibility is conditional: a model that converts successfully is not automatically supported, fast, or accurate at runtime.
Start with a reproducible configuration
Do not begin by changing precision, replacing an operator, or adding profiling flags. Record the complete software and hardware state first. Ascend behavior depends on the processor generation, SoC target, driver and firmware, CANN components, framework runtime, model representation, and input-shape policy.
| Item to record | What to capture | Why it matters |
|---|---|---|
| Hardware | Ascend product family, exact SoC, device count, and any Ascend virtual-instance configuration | Operator support, profiling options, and generated binaries can differ by product and partition. |
| Software | Driver, firmware, toolkit and runtime versions, and the CANN package used by ATC | Version compatibility is specific to the stack; a version number by itself does not prove that a model can run. |
| Model | Original framework and version, source format (for example, ONNX, Caffe, or TensorFlow), opset or graph variant, and offline-model filename | Conversion paths and required graph changes vary by source framework and operator set. |
| Shapes and precision | Batch size, static or dynamic shape mode, input and output data types, layouts, and ranges used in dynamic profiles | Support is documented by dimensions, formats, and data types, not only by an operator name. |
| Failure stage | Conversion, model load, first inference, numerical accuracy, throughput, latency, memory, or host-side time | Each stage uses different logs and diagnostic tools. |
| Evidence | ATC command and complete log, runtime error, accuracy comparison, warm-up policy, profile configuration, and summary files | Without the original conditions, a speed or compatibility conclusion cannot be reproduced. |
Keep this record with every test. Compare the installed environment with the release and version-mapping documentation for the exact product instead of inferring support from a familiar CANN release number.
Verify conversion and the target SoC
Use the actual soc_version
ATC must generate an offline model for the hardware that will execute it. A conversion command should pass the actual target value, not a nearby product name or a value copied from another machine. A minimal illustrative form is:
#1 Best Overall
- Powerful Turbo Fan:CAGIWIRU Electric Air Duster have reached speed up to 130000RPM,Can efficient remove dust and debris.With LED light and Three Adjustable Speed to meet with different cleaning tasks.
- With Attached USB C Fasting Charging cable.Charing 3 hours,Enjoy up to 240 minutes of use on the lowest setting, For third speed can use for 25mins.with four charging options to meet with your needs.
- Wide Application: attached 5 different nozzles,making it suitable for a variety of scenes, such as car,pet, pc, keyboards, and other electronic devices. It also serves for office and home clean duster.
- Handy Design & Portable:Cordless air duster electric nozzle is Light weight and small size, design and comfortable to hold and space saving, convenient to carry around. You can put it in your room or inside the car without taking up space.
- Economical &Reusable: Built durable materials with a long-lasting battery,our CAGIWIRU air duster it's a sustainable alternative to disposable air cans.
atc --model=net.onnx --soc_version={{actual_soc_version}}Add the framework, input, output, format, and shape arguments required by your model’s conversion guide. The SoC query reference in the CANN documentation explains how to identify the value to use. Save the complete command, standard output, standard error, and generated-file listing.
Successful conversion means that ATC built an offline model; it does not establish runtime support for every operator or shape. Confirm that the expected offline model was created, that its path is the one used by the application, and that model-load logs identify the intended device.
Account for graph-conversion exceptions
Some source graphs need more than a direct import. Certain Caffe models contain operators that are absent from the original framework, and original TensorFlow models with control-flow operators can require graph changes or an intermediate representation. Treat these as conversion-design problems: identify the unsupported node in the ATC log, consult the model-conversion guidance for that framework, and preserve a copy of the unmodified graph before making changes.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsUnderstand custom OPP lookup
When a custom operator package (custom OPP) is installed, the conversion guide states that custom OPP is searched preferentially before built-in OPP. Check the deployed package path and operator metadata when conversion appears to select an unexpected implementation. A stale or incorrectly packaged custom operator can therefore change conversion behavior even when the model graph has not changed.
Check CANN and offline-model version boundaries
An offline model converted by an earlier CANN package can run on a later package within the one-year compatibility period stated in the CANN Commercial Edition 8.0.0 ATC getting-started guide, last updated 2025-07-08. This is a software-compatibility rule, not a performance guarantee.
Rank #2
- Screen Cleaner is Your Screen’s New Bestfriend! - EVEO is proud to present a screen cleaner that’s perfectly suited for your TV. Meet your new screen cleaner, computer screen cleaner, laptop cleaner, iPad cleaner, Macbook,tv cleaner or any other electronic device. Our screen cleaner spray and wipe along with the included plush microfiber cloth, easily removes dust, fingerprints & other smudges on the screen’s surface. Clean effectively for a streak-free screen
- Gentle on your Screens - Our screen cleaner spray and wipes is compatible to be used even for the most sensitive LCD, LED, CRT, and OLED screens in proper use. Be at ease knowing that this product is compatible with all the major brands: LG, SONY, Samsung, Sharp, iPad, iPhone, Android screens, Kindle, PC monitors - we’ve got you covered! Can be used as LCD screen cleaner, tv cleaner, smart tv screen cleaner, and more
- Premium Super Soft Plush Microfiber Cloth - Included in this efficient screen cleaning kit. This screen cleaner spray and wipe has our signature plush microfiber cloth specially designed to comply with screens and monitors and leave them streak-free. Has been shown to effectively absorbs stubborn stains, zero streaks, and no fiber shedding. Always use this together with our screen cleaner for the best results
- Easy-to-Use Screen Cleaner Kit + microfiber cleaning cloth - The EVEO Screen Cleaning Kit will easily be your new favorite tool when it comes to taking care of the things you love most, quick and easy solution for a streak-free screen. Simply screw on the sprayer gun on the screen cleaner bottle. Spray the cleaning liquid onto the microfiber cloth, then wipe the screen until desired cleanliness & shine is achieved.
- Quality Screen Care Kit Worth Purchasing - Enjoy a convenient Screen Cleaner anytime, anywhere you need it. Your satisfaction is our top priority. We stand behind the quality of our products and that’s why, you shouldn’t be worrying at all. We manufacture the Best Screen Cleaner. and can be used for laptop cleaning kit, laptop screen cleaner, tv screen cleaner for smart tv. MacBook screen cleaner, monitor cleaner or screen cleaner spray for electronic devices with microfiber cleaning cloth
The same guide gives a narrower warning for dynamic-shape deployments and static Ascend virtual-instance scenarios: a model converted with CANN earlier than 6.0.1 cannot run on CANN 6.0.1 or later without reconversion using CANN 6.0.1 or later. Because that guidance is release-specific, check the current release notes for the exact product and deployment rather than extending the one-year or 6.0.1 statements to every present or future stack.
- If the model was converted on another host, record that host’s CANN package and ATC version.
- If a runtime upgrade follows, test the existing offline model before changing the model graph; reconvert only when the documented boundary or a runtime error requires it.
- For dynamic shapes, include the shape profile and virtual-instance mode in the compatibility record.
Prove operator support in the documented scope
For every failed, substituted, or unexpectedly slow node, open the operator documentation for the target product and compare all of these attributes:
Recommended Free Tools
- Product model or SoC generation.
- Input and output data types, including mixed-precision behavior.
- Input and output formats and layouts.
- Rank, dimensions, and permitted ranges.
- API or documentation version applicable to the installed CANN release.
A framework operator name is only a semantic label. It does not prove that the corresponding Ascend intermediate-representation operator exists, accepts the same attributes, or has an efficient implementation. The official operator documentation explicitly says that CANN does not guarantee performance outside the documented support scope, including product models, data types, data formats, and dimensions.
| Finding | Interpretation | Next action |
|---|---|---|
| Node is in scope for product, type, format, and dimensions | Functional support is plausible; poor speed may be caused by graph placement, data movement, or workload settings. | Run a correctness check, then profile the operator and surrounding transfers. |
| Node is outside a documented dimension, format, or type | Conversion may insert a fallback or produce an implementation with no performance guarantee. | Change the graph or tensor contract if accuracy permits, or plan a supported custom implementation. |
| No matching operator or required attribute | The framework graph cannot be treated as natively supported. | Investigate graph decomposition, an intermediate representation, or custom OPP deployment. |
Adapt and deploy a custom operator when support is missing
Moving a custom TBE operator between hardware generations is an engineering task, not a filename change. Compute APIs and datatype behavior can differ, so review and adapt the operator prototype, implementation, and operator information library for the target generation.
- Identify the exact unsupported or slow operator from conversion logs or a profile.
- Compare the source implementation with the target generation’s compute API and supported data types.
- Adapt the prototype, kernel, tiling or scheduling logic, and operator information as required by the target CANN release.
- Build the custom OPP using the toolchain for the deployment environment and verify that its package path is visible to ATC and runtime.
- Run single-operator tests on the target device with representative shapes, layouts, and precisions.
- Insert the operator into the full network and test graph-level accuracy, latency, throughput, memory, and failure recovery.
Do not declare success from a kernel test alone. A custom implementation can be numerically correct in isolation yet expose a layout, synchronization, or datatype problem when connected to neighboring nodes.
Rank #3
- 【Large size】：Measuring 800 x 300 mm/ 31. 5×12 inches, which is wider and taller than others. Large mouse pad can perfectly fit your keyboard and mouse of any size, and there is also ample space for peripherals such as phones, tablets, etc.
- 【LED backlight mouse pad】：Blade Hawks RGB mouse pad provides 7 static modes and 7 dynamic modes will give you the options you are looking for. Power-off memory function remembers the last lighting mode you selected. The super glow fiber Chroma will give you a colorful gaming setup that you want.
- 【Anti-slip rubber base】：Made from Natural rubber, this gaming mousepad will firmly grip your desktop, allowing you to enjoy the ultimate precision in the games you are most passionate about. It also comes with a texture that enhances this ability even further.
- 【Smooth and waterproof surface】: Micro-textured cloth surface, for extremely precise mouse control and a smooth movement. When liquid splashes on the gaming mouse pad, it forms water droplets and slides down. Will not delay your work or gameplay.
- 【Plug and play】: This mouse and keyboard pad is powered by USB, plug and play, no driver required. One button control light modes. Press one time to change the lighting mode, press twice to change the brightness, press and hold about 3 seconds turn ON/OFF.
Establish functional correctness before tuning speed
First make one inference path reliable. Use identical inputs and preprocessing on the reference framework and Ascend runtime, then compare outputs with tolerances appropriate to the model’s precision. Check the interface as well as the tensors:
- Input order, names, shape, layout, normalization, and data type.
- Output order, names, shape, post-processing, and any dequantization step.
- Runtime interface parameters, device selection, stream handling, and synchronization.
- Whether the application reads stale, uninitialized, truncated, or incorrectly formatted buffers.
The CANN tuning guidance identifies operator adaptation, model data reads and writes, and DVPP API usage as possible causes of application performance problems. The same boundaries can also corrupt inputs or outputs. Isolate preprocessing, inference, post-processing, and device-to-host transfers so an accuracy mismatch is assigned to the correct stage before optimization begins.
Keep a small regression set that includes ordinary inputs and edge cases. Record the reference output, Ascend output, absolute and relative error, and the first tensor at which the difference becomes material. A throughput number from an inaccurate pipeline is not a valid optimization result.
Profile the layer that can answer your question
Framework call stacks
For Python, PyTorch, or MindSpore call stacks, use the framework’s profiling API. The Ascend system-profiling documentation states that those framework-layer stacks cannot be profiled by the cited msprof system tool. Framework traces are therefore the right place to determine which application call, data-loader step, or model invocation is responsible for host-side time.
Device and system behavior
Use the msprof workflow and options documented for the actual product and CANN release when you need device-level evidence. Depending on the product, profiles can include AI Core and AI Vector Core metrics, CPU activity, memory, I/O, DVPP, and interconnect data. Applicability and option names vary by product.
Rank #4
- 【Softer and More Comfortable】Vaydeer wrist rest has unique diamond pattern, which is the combination of softness and aesthetics. The materials of wrist rest are improved into higher quality memory foam and covered with silky smooth lycra. The computer wrist rest makes you as comfortable and cushiony as like rest your wrists on clouds.
- 【Ergonomic Wrist Saver】The wrist rests for keyboard and mouse comes with a 17.32×3.15×0.83 inch keyboard wrist pad and a 5.94×3.15×0.83 inch mouse wrist support. Based on ergonomic design, the unique concave shape is the perfect fit for your wrist joints. The wrist rest pad fits most computer keyboards and laptops, improve hand and wrist posture, release your wrist and arm stress.
- 【Non-Slip Rubber Bottom】Featuring an anti-skid silicone base on the bottom, this wrist keyboard support stays firmly in place on your desk, preventing the padding from sliding around, ensuring stable and consistent wrist support during extended computer sessions.
- 【Better Experience & Pain Relief】Our keyboard arm rest is beneficial to alleviate the soreness caused by direct contact and friction between your arm and a hard desk surface, reducing the risk of wrist fatigue or carpal tunnel. The soft texture of memory foam can evenly distribute the pressure around your wrists and provide good support with just enough give.
- 【Helpful in Multiple Scenarios】Whether you're working, studying, writing, typing, gaming, this keyboard and mouse rest combo is an essential accessory to add comfort and support to your hands and wrists. It’s also a great gift for men, women, family, friend, coworker, gamer, teacher, etc.
Do not copy a profiling command from a different Ascend generation without checking its restrictions. The cited guide lists concrete differences for A2 and A3 products and documents --instr-profiling as mutually exclusive with multiple other options on those products. Remove conflicting flags and collect separate profiles when necessary; a command that accepts all flags is not evidence that every requested metric was captured.
Keep measurements comparable
- Use the same product, CANN release, model file, batch size, input shapes, precision, and host process.
- Warm up the model before timing steady-state inference and state how many warm-up and measured iterations were used.
- Separate first-load and compilation time from repeated-inference latency.
- Measure latency and throughput with the same synchronization points and report whether transfers and preprocessing are included.
- Change one variable at a time and retain the original profile.
The available documentation does not provide a general model benchmark, so do not quote a universal Ascend speedup or assume that an operator substitution improves every workload.
Find the slow Ascend operator
- Export the AI Core or AI CPU operator summary from the product-appropriate profiling workflow.
- Open the generated
op_summary_*.csvfile and inspect itsOP Typecolumn. This is the Ascend-supported operator type, which may differ from the framework node name. - Rank operators by device time, execution count, or accumulated time, depending on the question you are answering.
- Map the highest-cost operator back to its input shapes, formats, precision, and neighboring transfers in the trace.
- Check that operator’s documentation scope before modifying it. An out-of-scope shape or datatype can explain both a fallback and poor performance.
Interpret the summary with the timeline. A short kernel called thousands of times may dominate total time, while one long kernel may be caused by an oversized shape or synchronization. Check host waits, device-to-host copies, and DVPP activity before blaming the arithmetic kernel.
Trace an accuracy anomaly to its operator
Use the accuracy analyzer’s result_*.csv output to identify the row associated with the mismatch. The row’s NPUDump field points to dump data; inspect that dump to find the corresponding Ascend OpType. This creates a reproducible path from an output discrepancy to a concrete operator rather than guessing from the framework graph.
- Run the analyzer on the same input and reference output used for the correctness check.
- Locate the first materially divergent result row in
result_*.csv. - Follow its
NPUDumpvalue to the dump file and identify theOpType. - Compare that operator’s input and output tensors, precision, format, and supported dimensions with the documentation.
- If the operator is using an unsafe computation precision, apply a targeted precision adjustment, reconvert the model, and rerun inference.
The ATC FAQ gives --modify_mixlist as an example of blocking a faulty operator from a mixed-precision list. Treat this as a targeted experiment, not a universal fix: recheck numerical accuracy and measure latency and throughput after reconversion.
Best Value
- 【No Drill Mounted】Cable management tray can be assembled in as little as 3 minutes or less-simply. Side clips easily clampe the edge of desk, pass through the holes on the sides, and secure excess cables with baskets. Desk cable management supports inward or outward installation to meet your needs in different scenarios, and provides you with great convenience when collecting and organizing wires.
- 【Rubber Pad Protects Desk】Wire organizer under desk built-in soft rubber pads on the clamp protect your desk from scratches, so you will not damage your furniture or office.
- 【Sturdy and Beautiful】Cord management under desk is made of high-quality carbon steel and holds about 15 lbs. The wire basket has 2 holes on each side of the tray to enable your cables to pass freely. Cable baskets are suitable for desk thickness use from 0.4" to 2.4".
- 【Space Saving & Keep Organized】Desk cable management tray holds all your power cords, outlet strips, USB hubs and computer transformers hidden from your feet, and the data cables will not enter the vacuum cleaner to keep your desk clean and tidy. Desk wire organizer is also suitable for use in the kitchen and outdoors.
- 【Reduce Safety Risks】Cable tray under desk keeps all plugs away from your kids, no more worry about tripping. Using 2 holes wire tray saves extra space, helps you easily cabling and wire protection. The mesh design is not easy to accumulate dust, but avoids safety accidents caused by messy wires.
Choose a remedy using evidence
| Remedy | Support status | Accuracy risk | Performance question | Engineering effort | Compatibility check |
|---|---|---|---|---|---|
| Keep the existing operator and tune workload settings | Appropriate only when the operator and tensor contract are in documented scope. | Usually low if the graph and precision are unchanged. | Measure with fixed shapes, batch, warm-up, and synchronization. | Low. | Retest on the exact product and CANN release. |
| Change graph layout, shape, or precision | Valid only if the replacement configuration is documented for the target. | May alter model output or require recalibration. | Compare end-to-end latency, not only one kernel. | Medium; downstream nodes may also need changes. | Reconvert and rerun the complete correctness set. |
| Use a precision mix-list adjustment | Useful for an identified abnormal operator, subject to ATC and release rules. | Can improve accuracy while increasing compute cost or memory. | Measure the full network after reconversion. | Low to medium. | Confirm the option and mix-list format for the installed CANN version. |
| Build a custom OPP | Used when no suitable built-in implementation exists or adaptation is required. | Depends on kernel validation and accumulated numerical error. | Benchmark isolated and network-level behavior. | High. | Build, package, and test on the target hardware generation. |
| Modify the source graph or use an intermediate representation | Needed for certain framework-specific control-flow or missing-operator cases. | Graph rewrites can change semantics if not validated. | Check that removed conversions and transfers outweigh rewrite overhead. | Medium to high. | Preserve the source graph and document the conversion path. |
For every comparison, keep batch size, input shapes, precision, warm-up, and measurement method constant. A remedy that lowers one operator’s time but adds copies, synchronization, or post-processing can make the application slower overall.
Common symptoms and targeted checks
Conversion fails with an unsupported operator
- Confirm the source framework, model format, and exact node attributes in the ATC log.
- Check product, datatype, format, and dimension support in the matching operator documentation.
- Determine whether a framework-specific graph rewrite or intermediate representation is required.
- Use a custom OPP only after defining and testing its target-generation implementation.
Conversion succeeds but model loading fails
- Verify that the application loads the generated file, not an older file with the same name.
- Compare conversion and runtime CANN, driver, and firmware versions.
- Recheck dynamic-shape and virtual-instance version boundaries, especially when either side involves CANN 6.0.1 or later.
- Confirm that the offline model’s target SoC matches the executing device.
Inference runs but is inaccurate
- Check preprocessing, tensor names, layouts, data types, and output post-processing.
- Use
result_*.csvandNPUDumpto locate the first divergent operator. - Inspect precision and support scope for that operator, then test one targeted precision or graph change.
- Reconvert and rerun the complete accuracy set after every change.
Latency is poor despite successful inference
- Use the framework profiler for Python or framework call stacks and product-appropriate
msprofoptions for device behavior. - Inspect
op_summary_*.csvand theOP Typecolumn to find accumulated operator cost. - Look for repeated small kernels, host waits, memory copies, DVPP calls, and synchronization.
- Check whether the slow operator or shape is outside documented support scope.
Profiling command rejects options or produces incomplete data
- Read the profiling guide for the exact product and CANN release.
- Remove mutually exclusive options; on the cited A2/A3 workflows,
--instr-profilingcannot be combined with several other options. - Collect separate focused profiles rather than forcing every metric into one run.
Package a useful escalation report
A support engineer or team member can diagnose the issue faster when the report contains:
- Exact Ascend product and SoC, device count, and virtual-instance mode.
- Driver, firmware, toolkit, runtime, and CANN versions, including the package used for conversion.
- Original model format, framework version, model hash, ATC command, complete conversion log, and generated-file path.
- Static or dynamic shape configuration, representative input shapes, formats, datatypes, batch size, and precision policy.
- The failure stage and a minimal reproduction command or program.
- Reference-versus-Ascend accuracy results and the first divergent tensor, if applicable.
- Relevant
op_summary_*.csv, timeline, framework trace,result_*.csv, andNPUDumpfiles. - For custom operators, source revision, build output, OPP package path, single-operator tests, and network-level results.
This evidence distinguishes a conversion-support problem from a runtime-version mismatch, an out-of-scope operator, a data-movement bottleneck, and a numerical-precision issue. It also makes any claimed improvement reproducible on the same target.
Free tools Windows power users keep installed
One-click scans. No signup required.
A repeatable decision path
- Capture the hardware, software, model, shape, and failure-stage configuration.
- Confirm the target SoC and reproduce ATC conversion with the actual
soc_version. - Check version boundaries and current release notes for the exact product and deployment mode.
- Validate every suspect operator against product, type, format, dimension, and API scope.
- Establish correct inference and isolate preprocessing, transfers, inference, and post-processing.
- Profile framework and device layers with product-specific tools and non-conflicting options.
- Use
OP Typeinop_summary_*.csvfor speed issues andNPUDumptracing for accuracy issues. - Apply one documented graph, precision, or custom-operator change at a time, then reconvert and retest.
- Compare end-to-end accuracy, latency, throughput, and resource behavior under identical conditions.
Following this order prevents a common mistake: optimizing a model before proving that the target hardware, operator implementation, precision, and data path are valid. Ascend compatibility is a configuration-specific claim, and performance is an evidence-based result for a defined workload—not a property guaranteed by conversion alone.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
